# Tracking daily paths in home contexts with RSSI fingerprinting based on UWB through deep learning models

arXiv: https://arxiv.org/abs/2509.06161 · HTML: https://mlsanity.com/paper/2509.06161/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 82 references that are in the map.
- First posted on arXiv 7 Sep 2025.

## Builds on (most cited first, 6 of 6)

- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [A Survey of Indoor Localization Systems and Technologies](https://mlsanity.com/paper/1709.01015/) (2017)
- [A CNN-LSTM Quantifier for Single Access Point CSI Indoor Localization](https://mlsanity.com/paper/2005.06394/) (2020)
- [An RSSI-based Wireless Sensor Node Localisation using Trilateration and Multilateration Methods for Outdoor Environment](https://mlsanity.com/paper/1912.07801/) (2019)
- [Towards Ubiquitous Indoor Positioning: Comparing Systems across Heterogeneous Datasets](https://mlsanity.com/paper/2109.09436/) (2021)
- [Localization in Long-range Ultra Narrow Band IoT Networks using RSSI](https://mlsanity.com/paper/1703.02398/) (2017)

## Built on by (most cited first, 1 of 1)

- [Human Activity Recognition via Ultra-Wideband Data: A Framework for Dimensionality Reduction, Pattern Discovery, and Predictive Modeling](https://mlsanity.com/paper/2609.32008/) (2026)

## Abstract (arXiv)

The field of human activity recognition has evolved significantly, driven largely by advancements in Internet of Things (IoT) device technology, particularly in personal devices. This study investigates the use of ultra-wideband (UWB) technology for tracking inhabitant paths in home environments using deep learning models. UWB technology estimates user locations via time-of-flight and time-difference-of-arrival methods, which are significantly affected by the presence of walls and obstacles in real environments, reducing their precision. To address these challenges, we propose a fingerprinting-based approach utilizing received signal strength indicator (RSSI) data collected from inhabitants in two flats (60 m2 and 100 m2) while performing daily activities. We compare the performance of convolutional neural network (CNN), long short-term memory (LSTM), and hybrid CNN+LSTM models, as well as the use of Bluetooth technology. Additionally, we evaluate the impact of the type and duration of the temporal window (future, past, or a combination of both). Our results demonstrate a mean absolute error close to 50 cm, highlighting the superiority of the hybrid model in providing accurate location estimates, thus facilitating its application in daily human activity recognition in residential settings.